The Human Factors in the Adoption of Ambient Artificial Intelligence Scribe Technology: Towards Informed and User-centered Implementation of AI in Healthcare

Abstract

The landscape of healthcare documentation has undergone substantial transformations over the past few decades, evolving in parallel with technological advancements and shifts in healthcare delivery models. Central to these changes is the electronic medical record (EMR), a digital iteration of patients' paper charts that has become standard in healthcare settings. While EMRs are instrumental in streamlining data management and accessibility, they have introduced new challenges, particularly in terms of administrative burden on healthcare providers.

This thesis explores the integration of ambient artificial Intelligence (AI) scribe technology, a solution leveraging advancements in automatic speech recognition (ASR) and natural language processing (NLP), into physicians' workflows. AI scribes semi-automate the documentation process by capturing and synthesizing physician-patient interactions in real time, potentially alleviating the administrative workload on clinicians and improving the quality of care. The potential benefits of this technology are vast, and its adoption raises significant questions regarding privacy, consent, and trust, especially given its capability to record sensitive interactions in detail.

The study aims to (1) explore the integration of ambient scribe technology into physicians' workflows and assess its impact on physician-patient interactions, (2) identify and analyze the concerns related to privacy, consent, and trust among patients and physicians regarding the use of the technology, and (3) develop and evaluate a flexible informed consent protocol for patients and physicians. A mixed-method approach was employed, integrating quantitative data from surveys and qualitative insights from semi-structured interviews, providing a comprehensive understanding of the multifaceted impact of the technology.

The findings reveal that while AI scribes offer efficiency gains, particularly for complex and lengthy encounters, they are less beneficial for simple cases. Further, the efficiency of documentation with AI scribes compared to without is found to be dependent on individuals, with some physicians reporting negligible improvements due to extensive post-editing and the need for customization, while others noted notable gains. Regarding the impact on interaction, patients and physicians reported enhanced interactions due to reduced distractions but noted instances of self-censorship by patients due to discomfort with the recording process. Patients also expressed worry about self-censorship by physicians due to medicolegal concerns and unintended consequences due to technology over-reliance.

Concerning the second objective, patients and physicians expressed significant privacy concerns due to a lack of understanding and transparency in data handling policies. Patients also expressed concerns regarding the autonomy of private data, unauthorized access, and data breaches. The findings underscore the need for transparent data handling policies and robust security measures. Trust in physicians and pre-established patient-physician relationships also played a notable role in patient consent, with patients more likely to consent to AI scribe use with familiar physicians.

To address these concerns, the thesis proposed a Multi-Tier Granular Informed Consent (MTGIC) framework, integrating tiered and granular consent models to enhance transparency and participant control over personal data. The empirical evaluation of the MTGIC was well-received by both patients and physicians, though it necessitates ongoing refinement to improve usability and ensure it aligns with user needs.

In conclusion, while ambient scribe technology presents a promising tool for enhancing healthcare delivery, its successful implementation is contingent upon careful consideration of its integration into clinical workflows, the management of privacy concerns, and the development of effective consent processes. This study contributes to the ongoing discussion on the best practices for integrating emerging technologies into healthcare systems, aiming to enhance operational efficiency and patient care quality.